Today it is possible to generate very complex computer models of both single neurons and neural circuits. This has allowed researchers to examine the dynamics of neurons in ways not before possible. These models have been instrumental in advancing our understanding of the human nervous system at all levels. However, managing the ever-increasing model complexity has been problematic as it can easily outstrip our ability to meaningfully comprehend all of the intricacies the model represents. In effect, the advancements in computer technology have out-paced the advancements in the methods by which models are developed and analyzed. Our long-term goal is the development of the methods, technology, and infrastructure necessary to automate the neural model generation process. The objective of this research project is to focus on model characterization and simulation technology. The rationale for this project is that a rigorous model characterization process tailored to exploit the ever-increasing power of computing hardware enables detailed comparisons of experimental data and model alternatives, manageable forward progression of model complexity, and dramatically shorter model development times than are now possible. The project will pursue the following three aims: 1. Characterize neural models, where the objective is to exploit the changing dimensionality from model input to model output to improve the process of both parameter estimation and parameter sensitivity analysis. 2. Quantify Model and Simulator Robustness, where the objective is to examine the effect of model variation on model output. 3. Advance mainstream simulation platform technology, where the objective is to develop optimal hardware platforms for simulating neuron models. It is our expectation that the resulting set of analytical tools and simulation platform recommendations will be applicable to all types of neural models, will increase our understanding of these models, and decrease the time necessary to construct the models themselves.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS046851-04
Application #
7025734
Study Section
Special Emphasis Panel (ZRG1-SSS-E (95))
Program Officer
Liu, Yuan
Project Start
2003-04-01
Project End
2008-02-28
Budget Start
2006-03-01
Budget End
2008-02-28
Support Year
4
Fiscal Year
2006
Total Cost
$282,165
Indirect Cost
Name
Georgia Institute of Technology
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
097394084
City
Atlanta
State
GA
Country
United States
Zip Code
30332
Mitchell, Cassie S; Lee, Robert H (2009) A quantitative examination of the role of cargo-exerted forces in axonal transport. J Theor Biol 257:430-7
Wester, B A; Lee, R H; LaPlaca, M C (2009) Development and characterization of in vivo flexible electrodes compatible with large tissue displacements. J Neural Eng 6:024002
Mitchell, Cassie S; Lee, Robert H (2008) Pathology dynamics predict spinal cord injury therapeutic success. J Neurotrauma 25:1483-97
Mitchell, Cassie S; Feng, Steve S; Lee, Robert H (2007) An analysis of glutamate spillover on the N-methyl-D-aspartate receptors at the cerebellar glomerulus. J Neural Eng 4:276-82
Sorensen, M E; DeWeerth, S P (2007) Functional consequences of model complexity in rhythmic systems: I. Systematic reduction of a bursting neuron model. J Neural Eng 4:179-88
Ban, Lan; Shapiro, Nicholas P; Lee, Robert H (2007) Parsimonious design principles for motor unit models. Conf Proc IEEE Eng Med Biol Soc 2007:2412-5
Mitchell, Cassie S; Lee, Robert H (2007) Output-based comparison of alternative kinetic schemes for the NMDA receptor within a glutamate spillover model. J Neural Eng 4:380-9
Reid, Michael S; Brown, Edgar A; DeWeerth, Stephen P (2007) A parameter-space search algorithm tested on a Hodgkin-Huxley model. Biol Cybern 96:625-34
DeWeerth, Stephen P; Reid, Michael S; Brown, Edgar A et al. (2007) A comparative analysis of multi-conductance neuronal models in silico. Biol Cybern 96:181-94
Weinstein, Randall K; Reid, Michael S; Lee, Robert H (2007) Methodology and design flow for assisted neural-model implementations in FPGAs. IEEE Trans Neural Syst Rehabil Eng 15:83-93

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